Supplementing measurement results of automated analyzers

ABSTRACT

A computer-implemented method for supplementing measurement results of automated analyzers is presented. The method includes obtaining, at a computer device, a result of a measurement performed by an automated analyzer, the computer device and the automated analyzer being located within a privileged computer network, obtaining a context related algorithm associated with the result of the measurement defining one or more triggering conditions and context related information from a computer device residing outside of the privileged computer network at the computer device and processing the result of the measurement by using the context related algorithm to generate a context specific supplement to the result of the measurement at the computer device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/810,788, filed Nov. 13, 2017, now allowed, which claims priority toEP 16200295.0, filed Nov. 23, 2016, which are hereby incorporated byreference.

BACKGROUND

The present disclosure relates to methods and systems for supplementingmeasurement results of automated analyzers.

Automated analyzers play an important role in today's laboratoryenvironments. The measurement results of these automated analyzers areused, e.g., by medical practitioners, to make therapy decisions. Eventhough medical practitioners are usually highly trained, errors mighthappen in the process of interpreting the measurement result ofautomated analyzers. These errors can lead to grave consequences for thepatient.

SUMMARY

According to the present disclosure, a computer-implemented method forsupplementing measurement results of diagnostic or laboratory automatedanalyzers is presented. The method can comprise obtaining, at a computerdevice, a result of a measurement performed by a diagnostic orlaboratory automated analyzer. The computer device and the automatedanalyzer can be located within a privileged computer network. The methodcan also comprise obtaining a context related algorithm associated withthe result of the measurement defining one or more triggering conditionsand context related information from another computer device thatresides outside of the privileged computer network at the computerdevice and processing the result of a measurement by the diagnostic orlaboratory automated analyzer by using the context related algorithm togenerate a context specific supplement to the result of the measurementat the computer device.

Other features of the embodiments of the present disclosure will beapparent in light of the description of the disclosure embodied herein.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The following detailed description of specific embodiments of thepresent disclosure can be best understood when read in conjunction withthe following drawings, where like structure is indicated with likereference numerals and in which:

FIG. 1 illustrates schematically an exchange of information betweencomputer devices inside and outside a privileged computer networkaccording to an embodiment of the present disclosure.

FIG. 2 illustrates a schematic diagram a network environment accordingto an embodiment of the present disclosure.

FIG. 3 illustrates a swim lane diagram illustrating the methodsaccording to an embodiment of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description of the embodiments, reference ismade to the accompanying drawings that form a part hereof, and in whichare shown by way of illustration, and not by way of limitation, specificembodiments in which the disclosure may be practiced. It is to beunderstood that other embodiments may be utilized and that logical,mechanical and electrical changes may be made without departing from thespirit and scope of the present disclosure.

A computer-implemented method for supplementing measurement results ofautomated analyzers is presented. The method can include obtaining, at acomputer device, a result of a measurement performed by an automatedanalyzer, the computer device and the automated analyzer being locatedwithin a privileged computer network, obtaining a context relatedalgorithm associated with the result of the measurement defining one ormore triggering conditions and context related information from acomputer device residing outside of the privileged computer network atthe computer device and processing the result of the measurement byusing the context related algorithm to generate a context specificsupplement to the result of the measurement at the computer device. Acomputer network can be configured to carry out the above method.

Some advantages of the above method can be, firstly, the method canrender measurement results issued by automated analyzers moreinformative by adding context specific supplements. This, in turn, mayimprove a quality of therapy or other decisions based on the measurementresults and/or reduce a number of errors when the measurement resultsare interpreted in some examples.

Secondly, the method can improve quality context specific supplementsprovided by automated analyzers in some examples. For example, anautomated analyzer may only use algorithms authored by certain sourcesto supplement its measurement results.

Thirdly, the method can allow for updating the algorithms used byautomated analyzers in a simpler manner in some examples. In some priorart automated analyzers, algorithms used to supplement measurementresults are included in the instrument software delivered with aninstrument. However, this software might not be updated regularly and/oran operator might have no influence on the content of the updates. Themethod can facilitate a continuous update process of specific andselected algorithms for generating supplements to measurement results insome examples.

Fourthly, the method can allow for using software developed on privatedata (e.g., patient data) in other networks located outside a privilegednetwork the software is developed in (e.g., a hospital or laboratorynetwork). In this matter, a knowledge base used to supplement themeasurement results of the automated analyzer can be increased in someexamples. This may further improve a usefulness of the generatedsupplements.

Fifthly, the method can involve collecting and using statistical data insupplements of measurement results of automated analyzers which can alsoimprove the usefulness of these results.

The term ‘diagnostic or laboratory automated analyzer’ as used hereincan refer to any kind of automated or semi-automated technical device togenerate measurement results in a laboratory or other health-carerelated environment.

The term ‘diagnostic automated analyzer’ can not only include automatedanalyzers used in the process of diagnosing a disease, but alsoautomated analyzers for screening, health classification, riskassessment, monitoring, staging, prediction, prognosis and more. Forexample, a diagnostic automated analyzer can be an ultrasound device, aradiology device (e.g., an x-ray device, a computer tomography device ora MRI device), an ECG device or an EEG device or another monitoringdevice of a bodily function.

A ‘laboratory automated analyzer’ can be any automated analyzer used inlaboratory work in the clinical, chemical, biological, immunology orpharmaceutical area or the like. For example, ‘automated analyzers’ caninclude in-vitro diagnostic analyzers,

‘Automated analyzers’ may not be necessarily is located in a dedicatedlaboratory or clinical environment. Rather, the term can also includestand-alone analyzers for carrying out diagnostic or analytic proceduresin the clinical, chemical, biological, immunology or pharmaceuticalarea. For example, a benchtop device in point-of-care settings such asphysician clinics or pharmacies or a device for home-use can also be apiece of laboratory equipment according to the present disclosure.

‘Automated analyzers’ as used herein can comprise a control unit orcontroller operatively coupled to one or more analytical, pre- andpost-analytical work cells. The control unit can be operable to controlthe work cells. In addition, the control unit may be operable toevaluate and/or process gathered analysis data, to control the loading,storing and/or unloading of samples to and/or from any one of theanalyzers, to initialize an analysis or hardware or software operationsof the analysis system used for preparing the samples, sample tubes orreagents for the analysis and the like.

The term ‘analyzer’/‘analytical work cell’ as used herein can encompassany apparatus, or apparatus component, that can induce a reaction of abiological sample with a reagent for obtaining a measurement value. Ananalyzer can be operable to determine via various chemical, biological,physical, optical or other technical procedures a parameter value of thesample or a component thereof. An analyzer may be operable to measurethe parameter of the sample or of at least one analyte and return theobtained measurement value. The list of possible analysis resultsreturned by the analyzer can comprise, without limitation,concentrations of the analyte in the sample, a digital (yes or no)result indicating the existence of the analyte in the sample(corresponding to a concentration above the detection level), opticalparameters, DNA or RNA sequences, data obtained from mass spectroscopyof proteins or metabolites and physical or chemical parameters ofvarious types. An analytical work cell may comprise units assisting withthe pipetting, dosing, and mixing of samples and/or reagents. Theanalyzer may comprise a reagent holding unit for holding reagents toperform the assays. Reagents may be arranged for example in the form ofcontainers or cassettes containing individual reagents or group ofreagents, placed in appropriate receptacles or positions within astorage compartment or conveyor. It may comprise a consumable feedingunit. The analyzer may comprise a process and detection system whoseworkflow can be optimized for certain types of analysis. Examples ofsuch analyzer are clinical chemistry analyzers, coagulation chemistryanalyzers, immunochemistry analyzers, urine analyzers, nucleic acidanalyzers, used to detect the result of chemical or biological reactionsor to monitor the progress of chemical or biological reactions.

The term ‘privileged (computer) network’ as used herein can refer to anycomputer network having a barrier between it and another outsidenetwork. The privileged computer network can be a trusted, secureinternal network and the other outside network, such as the Internet,can be assumed not to be secure or trusted. A privileged (computer)network can be protected by a firewall or another network protectionmeans.

For example, a privileged (computer) network can be a hospital networkor a laboratory network (also referred to as ‘hospital informationsystem’ and ‘laboratory information system’ herein). In these examples,patient data or similar data can be accessible at computer deviceswithin the privileged (computer) network (possibly subject toauthentication or other access requirements) but not from a computerdevice outside the privileged (computer) network.

Accordingly, the term ‘non-privileged (computer) network’ as used hereincan refer to any computer network which is not a privileged computernetwork.

The term ‘computer network’ as used herein can encompass any type ofwireless network, such as a WIFI, GSM, UMTS or other wireless digitalnetwork or a cable based network, such as Ethernet or the like. Inparticular, the communication network can implement the Internetprotocol (IP). For example, the communication network can comprise acombination of cable-based and wireless networks.

A ‘control unit’ or ‘controller’ can control the automated orsemi-automated system in a way that the necessary steps for theprocessing protocols can be conducted by the automated system. That canmean the control unit may, for example, instruct the automated system toconduct certain pipetting steps to mix the liquid biological sample withreagents, or the control unit can control the automated system toincubate the sample mixtures for a certain time and the like. Thecontrol unit may receive information from a data management unitregarding which steps may need to be performed with a certain sample. Insome embodiments, the control unit may be integral with the datamanagement unit or may be embodied by a common hardware. The controlunit may, for instance, be embodied as a programmable logic controllerrunning a computer-readable program provided with instructions toperform operations in accordance with a process operation plan. Thecontrol unit may be set up to control, for example, any one or more ofthe following operations: loading and/or wasting and/or washing ofcuvettes and/or pipette tips, moving and/or opening of sample tubes andreagent cassettes, pipetting of samples and/or reagents, mixing ofsamples and/or reagents, washing pipetting needles or tips, washingmixing paddles, controlling of a light source, e.g. selection of thewavelength, or the like. In particular, the control unit may include ascheduler, for executing a sequence of steps within a predefined cycletime. The control unit may further determine the order of samples to beprocessed according to the assay type, urgency, and the like.

A ‘measurement result’ of the diagnostic or laboratory automatedanalyzer as used herein can be any output the above listed automatedanalyzer. Depending on the respective automated analyzer, a measurementresult can be obtained by analyzing live or dead body or a part thereof(e.g., a mammalian patient or a part of a mammalian patient) or a sample(e.g., a biological sample).

For instance, a measurement result can include one or more parametervalues measured in a live or dead body or a part thereof or a sample(e.g., a concentration of a particular substance in a blood sample). Inother examples, a measurement result can include one or more images of alive or dead body or a part thereof or a sample (e.g., an X-ray or MRIimage).

The term ‘sample’ can refer to material(s) that may potentially containan analyte of interest. The sample can be derived from a biologicalsource, such as a physiological fluid, including blood, saliva, ocularlens fluid, cerebrospinal fluid, sweat, urine, stool, semen, milk,ascites fluid, mucous, synovial fluid, peritoneal fluid, amniotic fluid,tissue, cultured cells, or the like. The biological sample can bepretreated prior to use, such as preparing plasma from blood. Methods oftreatment can involve centrifugation, filtration, distillation,dilution, concentration and/or separation of sample components includinganalytes of interest, inactivation of interfering components, and theaddition of reagents. A sample may be used directly as obtained from thesource or used following a pretreatment to modify the character of thesample. In some embodiments, an initially solid or semi-solid biologicalmaterial can be rendered liquid by dissolving or suspending it with asuitable liquid medium. In some examples, the sample can be suspected tocontain a certain antigen or nucleic acid.

The term ‘order’ can include any request for a piece of laboratoryequipment to automatically or semi-automatically carry out a particulartask. For example, an order can be a request that one or more assays areto be performed on one or more biological samples.

The methods and systems for supplementing measurement results accordingto the present disclosure will be discussed in connection with FIG. 1and FIG. 3. Subsequently, different additional aspects will be discussedin connection with FIG. 2 and FIG. 3.

FIG. 1 schematically illustrates an exchange of information 12, 10between computer devices 3 a, 3 b inside and outside a privilegedcomputer network 1 (PRIS) according to the present disclosure. In theexample of FIG. 1, the communication can take place with a computerdevice in a non-privileged computer network 2 (NOPRIS). In the exampleof FIG. 1, a first computer device 3 a can be an automated analyzer(e.g., an in-vitro automated analyzer). A second computer device 3 b canbe a user terminal in the privileged computer network 1 (PRIS). Forexample, the user terminal can be a desktop computer or a mobile device(e.g., a tablet device, a laptop or a smartphone). The privilegedcomputer network 1 (PRIS) can be a hospital network or a laboratorynetwork (also referred to as hospital information system (HIS) orlaboratory information system (LIS) herein).

It can be noted that the particular devices and networks are onlyexamples of computer devices and networks used for the sake ofillustration. The techniques of the present disclosure can also be usedin other environments (e.g., as discussed below in connection with FIG.2).

As can be seen, the method can comprise obtaining (see step 101 in FIG.3), at the user terminal 3 b, a result of a measurement 11 performed bythe automated analyzer 3 a (e.g., a result of any assay of an in-vitroanalyzer or any of the other measurement results discussed above). In afurther step (see step 102 in FIG. 3), information regarding the resultof the measurement 12 can be transmitted to a computer device residingoutside of the privileged computer network 1 in the non-privilegednetwork 2.

However, in other examples, no information regarding the result of themeasurement 12 can be provided outside the privileged computer network1. The techniques of the present disclosure can still be applied withoutthis step (besides the aspects relying on the provision of informationregarding the result of the measurement to a computer device in thenon-privileged network).

Moreover, the user terminal 3 b can receive (see step 103 in FIG. 3) acontext related algorithm associated with the result of the measurementdefining one or more triggering conditions 6 and context relatedinformation 7 from the computer device residing outside of theprivileged computer network 1. Then, the user terminal 3 b (or anothercomputer device inside the privileged computer network 1) can process(see step 104 in FIG. 3) the result of the measurement by using thecontext related algorithm to generate a context specific supplement tothe result of the measurement 11 at the computer device.

The different steps and elements of the technique for supplementingmeasurement results according to the present disclosure can subsequentlybe discussed in more detail.

The information regarding the result of the measurement 12 will bediscussed first. As can be seen in FIG. 1, the information regarding theresult of the measurement 12 may include anonymized informationregarding the result of the measurement 4 and a unique identifier 5.

The anonymized information regarding the result of the measurement 4 canbe generated by removing identifying specific information regarding apatient from the measurement result 11 and supplementing the result by aunique identifier and potentially other generic identifiers for thesample. In the following, it can be assumed that the measurement result11 has been obtained by analyzing a patient or a sample associated witha patient. However, the techniques of the present disclosure may not belimited to situations in which the analysis object is a patient. Otherscenarios are, e.g., discussed above.

In other words, the measurement result 11 can be de-identified beforebeing provided to the computer device outside the privileged computernetwork 1. This process can include removing, encrypting and/orobfuscating any information suitable to identify a patient associatedwith the measurement result 11. For instance, the anonymization processcan include removing or encrypting a name of the patient, an address ofthe patient, contact data of the patient, a social security or insurancecarrier number or other information suitable to identify the patient.

The remaining anonymized information 4 can include one or more of: oneor more measurement values determined by the automated analyzer, animage obtained by the automated analyzer and a description of the resultof the measurement of the automated analyzer, or meta-data processedbased on or one or more of these pieces of information. In one example,anonymized information 4 can include the results of one or more assaysconducted on a sample (e.g., an assay of an in-vitro analyzer).

In addition, the anonymized information 4 can include additionalinformation associated with the result of the measurement 11. In oneexample, the additional information can include clinical informationassociated with the result of the measurement 11. For instance, theclinical information can include biometric data (e.g., one or more ofgender, age, race, height, weight or other biometric data relating tothe patient), information regarding physical symptoms and findings ofthe patient, information regarding the anamnesis and ongoing treatmentof the patient (e.g., one or more of past and ongoing treatments, pastsurgical procedures, prescribed drugs, past or current diagnosticinformation regarding the patient and other related information).

However, in other examples, some of the clinical information (or allclinical information in some examples) associated with the result of themeasurement may not leave the privileged computer network 1. In theseexamples, such information cannot be provided to the computer deviceoutside the privileged computer network 1. In addition or alternatively,certain clinical information can be used to identify the patient in someexamples. Thus, the anonymized information 4 can be free of this type ofinformation in some examples.

In one example, the anonymized information 4 can include the result ofany assay or other analytical information obtained by the automatedanalyzer and information regarding a diagnosis for the patient (e.g.,ICD coded information).

In addition or alternatively, the anonymized information 4 can includeinformation regarding the automated analyzer having performed themeasurement yielding the measurement result (e.g., an identity of amanufacturer, a model number or a unique identifier of the device). Inaddition or alternatively, the anonymized information 4 can includeinformation regarding a state of the automated analyzer having performedthe measurement yielding the measurement result (e.g., informationregarding disposables used by the automated analyzer or measurementparameters used by the automated analyzer).

The unique identifier 5 can be any data item suitable to identify aparticular information regarding the result of the measurement 12 sentto the computer device outside the privileged computer network 1.

The computer device residing outside of the privileged computer network(e.g., in the non-privileged computer network 2) can process theinformation regarding the result of the measurement 12, as will bediscussed in more detail below. Before that, in the subsequent sections,the information 10 received at the privileged computer network 1 fromthe computer device residing outside of the privileged computer network(e.g., in the non-privileged computer network 2) can be explained inmore detail.

This information 10 can include a context related algorithm associatedwith the result of the measurement defining the one or more triggeringconditions 6 and context related information 7.

The context related algorithm can be provided as a computer programwhich can be executed by the user terminal 3 a in the privilegedcomputer network 1 to generate a context specific supplement to theresult of the measurement 11. For example, the context related algorithmcan be provided as a stand-alone program (e.g., an application for amobile device or a stand-alone program for another a computer device).In other examples, the context related algorithm can be provided as anadd-on to a computer program already installed on a compute device inthe privileged computer network. For instance, the computer program maybe a presentation, or analysis, tool for measurement results ofautomated analyzers. The context related algorithm can be added to thefunctionalities of this presentation, or analysis, tool (additionaldetails will be discussed below in connection with the generation of thesupplement to the result of the measurement).

In some examples, the context related algorithm can be provided in aform suitable to be transmitted over a network which may need to befurther processed at the user terminal 3 a in the privileged computernetwork 1 before it can be used to supplement the measurement results.For instance, it may be necessary to compile, install and/or setup thecontext related algorithm in some examples. In other examples, thecontext related algorithm can be provided as executable code.

The one or more triggering conditions 6 can define one or more criteriafor the result of the measurement in some examples. In some examples,the one or more criteria can include one or more of: a criterionevaluating a threshold for one or more measurement values, a criteriondetermining a relationship between two or more measurement values and acriterion defining a pattern in a result of a measurement.

For instance, a triggering condition can be that an analyteconcentration in a sample lies below or above a predetermined threshold,or in a predetermined concentration range. In other examples, thetriggering condition can be the presence of one or more analytes in asample. In still other examples, the triggering condition can be thepresence of a characteristic in a waveform included in the measurementresult (e.g., of an EEG or an EEC waveform). In still other examples,the triggering condition can be the presence of a feature of an image ofthe measurement result (e.g., a feature in an X-ray or MRI image).

In still other examples, the one or more triggering conditions 6 candefine criteria not only for the measurement result 11 but also forresults of additional measurements (e.g., a second analyte concentrationbeing the measurement result of another assay). In other words, the oneor more triggering conditions 6 can define criteria for a combination ofmeasurement results of different sources, or of the same source atdifferent times (e.g., a first assay at an earlier time and a secondassay of the same type at a later time).

In addition or alternatively, the one or more triggering conditions 6can define criteria for clinical data and/or other patient dataassociated with the measurement result 11 (e.g., the clinical and/orpatient data discussed above). For example, a triggering condition 6 candefine a criterion for the measurement result 11 and biometric dataassociated with the patient (or other patient data). An example of thistriggering condition may include a first criterion regarding the resultof an assay (e.g., concentration of analyte X is above a thresholdvalue) and a second criterion regarding patient data (e.g., patient isolder than threshold age Y). In other words, the triggering conditions 6can define criteria for a combination of measurement results withclinical and/or patient data.

Subsequently, the context related information 7 defined by the contextrelated algorithm will be discussed in more detail. The context relatedinformation 7 can include one or more of: explanatory informationregarding the interpretation of the measurement result of the automatedanalyzer, proposals regarding diagnostic steps to be undertaken (e.g.,proposals regarding additional measurements to be carried out),proposals regarding treatment steps to be carried out, informationregarding potential errors that occurred while performing themeasurement by the automated analyzer, or additional information relatedto the measurement result or clinical data associated with themeasurement result.

In one illustrative example, the measurement result 11 is aninternational normalized ratio (INR) determined by a coagulationanalyzer. A particular context related algorithm can include atriggering condition defining a particular INR range (e.g., INR>2 andINR<3) and define a context related information 7 associated with thetriggering condition in the form of a textual information “INR is in thetherapeutic range for preventive oral anticoagulation with Wafarin.” Inthis example, the measurement result including an INR for a patient maybe supplemented with this textual information when the measurementresult 11 is processed by using the context related algorithm. Thisprocess will be explained in more detail.

As discussed above, the technique of present disclosure can includeprocessing the result of the measurement by using the context relatedalgorithm to generate a context specific supplement to the result of themeasurement at the computer device. It has been discussed above (furtherdetails will be discussed below) that one or more context relatedalgorithms can be obtained at a computer device (e.g., user terminal 3a) inside the privileged computer network 1. In one example, a pluralityof context related algorithms can be obtained from the computer deviceoutside the privileged computer network 1 and provided for use insidethe privileged computer network 1.

The computer devices of the privileged computer network 1 (e.g., userterminal 3 a or another computer device located inside the privilegedcomputer network 1) can now apply the context related algorithms toprocess a result of a measurement by a diagnostic or laboratoryautomated analyzer in the privileged computer network 1.

This can involve checking if a triggering condition is met by the resultof a measurement 11. As discussed above, this can include processing(see step 104 in FIG. 3) the result of a measurement (e.g., acalculation meta-data based on the result of a measurement or featureextraction of features of the result of a measurement).

In other examples, the processing can involve checking if a triggeringcondition is met by results of additional measurements associated withthe result of a measurement (e.g., belonging to the same patient).Alternatively or in addition, the processing can involve checking if atriggering condition is met by clinical or other patient data associatedwith the result of a measurement (e.g., belonging to the same patient).Further examples are discussed above in connection with the triggeringconditions 6.

If the one or more triggering conditions 6 are met, the context relatedinformation 7 can be processed into the context specific supplement tothe result of the measurement. In one example, this can involve addingtextual information contained in the context related information 7 tothe result of the measurement (as in the INR example above). However,the context specific supplement can also include additional oralternative items other than textual information. In one example, thecontext related supplement can include an audible, visual or audiovisualwarning. In another example, the context related supplement can includea hyperlink linking to additional resources (e.g., regardinginterpretation of the measurement results or statistical data). In stillother examples, the context related supplement can include a furtheralgorithm whose execution can be triggered by a user (e.g., ordering arepeated measurement or further diagnostic steps or an analysis of themeasurement result and/or additional clinical data of the patient).

In one example, an indicator can be presented to a user at a computerdevice 3 a inside the privileged computer network 1 that a contextspecific supplement is available for the result of the measurement on auser interface of the computer device 3 a. For instance, an icon orother indicator may be displayed on a user interface of the computerdevice 3 a. In response to a user interaction with the user interface ofthe computer device (e.g., pressing the icon or hovering over the icon),the context specific supplement can be presented to a user (see step 105in FIG. 3). In other examples, the context specific supplement can beautomatically presented to a user when an associated measurement resultis presented (e.g., on a graphical user interface of the automatedanalyzer or another device in the privileged computer network).

In one example, the processing of the result of the measurement usingthe context related algorithm can be performed automatically (i.e.,without user interaction).

In the following passages, additional details regarding process toretrieve context specific algorithms will be discussed.

In one example, one or more context related algorithms can be obtainedautomatically from the computer device residing outside of theprivileged computer network 1. The process of obtaining the contextrelated algorithms can happen continuously. For example, context relatedalgorithms can be obtained at predetermined points in time or uponoccurrence of a predetermined event (e.g., when an automated analyzerperforms a particular measurement).

In another example, the computer device residing outside of theprivileged computer network 1 (e.g., in the non-privileged network 2)can be continuously or regularly checked for new or updated contextrelated algorithms. Then, if a new or updated context related algorithmis available at the computer device residing outside of the privilegedcomputer network 1 (e.g., in the non-privileged network 2), this contextrelated algorithm can be retrieved by a computer device inside theprivileged computer network 1.

In other examples, a user inside the privileged computer network 1 mayhave to confirm that context related algorithms can be obtained. Instill other examples, a user inside the privileged computer network 1may access a user interface presented by the computer device of outsideof the privileged computer network 1 and select one or more contextrelated algorithms to be obtained.

In addition or alternatively, a user inside the privileged computernetwork 1 can select one or more sources whose context relatedalgorithms can be obtained at the computer device 3 a. In these cases,context related algorithms from the selected one or more sources (e.g.,a particular author of context related algorithms) can be obtained ifthey are available at the computer device residing outside of theprivileged computer network 1 (e.g., in the non-privileged network 2),e.g., continuously or upon a predetermined trigger event. In otherwords, a user can subscribe context related algorithms of one or moresources. Further explanations regarding this aspect will be given inconnection with FIG. 2.

In one example, the computer device residing outside of the privilegedcomputer network 1 (e.g., in the non-privileged network 2) can include arepository of context related algorithms defining one or more triggeringconditions and context related information. For instance, the contextrelated algorithms in the repository may have been created in privilegedcomputer networks other than the privileged computer system 1. In thismanner, the users inside the privileged computer system 1 can gainaccess to a large number of context related algorithms which can beupdate in a regular manner.

It has been explained above that the context specific supplement caninclude particular information derived from the context relatedinformation of a context related algorithm. In addition oralternatively, the context specific supplement can also includestatistical information 9 associated with the result of the measurement.

In one example, the statistical information associated with the resultof the measurement can include information regarding a likelihood of acombination of measurement values in the result of the measurement withother measurement values. For instance, the statistical information mayindicate that the results of two different measurements of the samesample or associated to the same patient have a likelihood being below apredetermined threshold. By adding this statistical information to asupplement of a measurement result, a user may be able to identifyerroneous results.

In addition or alternatively, the statistical information associatedwith the result of the measurement can include information regarding alikelihood of a combination of a measurement value in the result of themeasurement with a particular diagnosis. For instance, the statisticaldata may indicate that a particular diagnosis is not compatible with apredetermined measurement result (e.g., a combined likelihood of thediagnosis being correct and the measurement result being as it is belowa predetermined threshold). In other examples, the statisticalinformation may include a list of one or more likely and/or one or moreunlikely diagnoses compatible with the particular measurement result.

In addition or alternatively, the statistical information associatedwith the result of the measurement can include information regarding alikelihood of a combination of a measurement value of the result with aparticular diagnosis or clinical finding. As in the examples above, thedata may indicate that a particular diagnosis or clinical finding is notcompatible with a predetermined measurement result, or a list of one ormore likely and/or one or more unlikely disease progressions or clinicalfindings compatible with the particular measurement result.

In addition or alternatively, the statistical information associatedwith the result of the measurement can include information regarding alikelihood of a combination of a measurement value of the result of themeasurement with particular biometric data (e.g., of the same patient).As in the examples above, the data may indicate that a particular valuebiometric data is not compatible with a predetermined measurement resultor a list of one or more likely and/or one or more unlikely values ofranges or values of biometric data compatible with the particularmeasurement result.

In addition or alternatively, the statistical information associatedwith the result of the measurement statistical information regardingother measurements carried out in combination with the measurementhaving yielded the result. In this example, the supplement to themeasurement result can include one or more propositions for additionalmeasurements to be carried out.

In addition or alternatively, the statistical information associatedwith the result of the measurement statistical information regardingnext diagnostic steps carried out after the measurement having yieldedthe result. In this example, the supplement to the measurement resultcan include one or more propositions for additional diagnostic steps.

As can be seen in the above examples, the supplement to the measurementresult can include multiple different items of statistical informationrelated to the result of the measurement. A medical practitioner orother user may find this statistical information helpful to interpretthe measurement results of automated analyzers.

In the above examples, the statistical information can relate toclinical information contained in or related to the measurement result.In addition or alternatively, the statistical information associatedwith the context related algorithm can include meta-data regarding thecontext related algorithm. For example, the statistical information caninclude one or more of: information regarding a frequency with which aparticular context related algorithm has been obtained and statisticalinformation related to a source of the particular context relatedalgorithm. This information can also be added to the context relatedsupplement. In this manner, a user may get additional informationregarding a quality and/or trustworthiness of the statisticalinformation. In other examples (or in addition), statistical informationregarding context related algorithms can be presented on a userinterface of the computer device outside the privileged computer networkhosting the repository of context related algorithms.

In connection with FIG. 2 different possible processes to collect thestatistical information will be described in more detail.

In the preceding sections, aspects of the interaction of a computerdevice located in one privileged computer network to obtain contextspecific algorithms have been discussed in connection with FIG. 1.Subsequently, in connection with FIG. 2, different techniques involvingusers in multiple privileged computer networks will be treated in moredetail, as well as aspects of the generation of context specificalgorithms and statistical information.

As can be seen in FIG. 2, the computer device residing in thenon-privileged network 2 can be networked with multiple privilegedcomputer networks 1 a-1 c (PRIS #1, PRIS #2, and PRIS #3). Each of themultiple privileged computer networks 1 a-1 c can be configured asdescribed for the privileged computer network 1 of FIG. 1 above. Forinstance, each of the multiple privileged computer networks 1 a-1 c canbe a hospital information system or a laboratory information system.Users in each of the multiple privileged computer networks 1 a-1 c mayobtain context related algorithms and statistical information from thecomputer device residing in the non-privileged network 2 (e.g., from arepository provided by the computer device residing in thenon-privileged network 2).

In addition, the techniques of the present disclosure can allow fordistributing content between different privileged computer networks 1a-1 c. For example, a user in a first privileged computer network (e.g.,PRIS #1) can create one or more context related algorithms and providethem to the computer devices residing in the non-privileged network 2.In the same manner, the computer device residing in the non-privilegednetwork 2 can collect context related algorithms generated by otherusers in other privileged computer networks (e.g., PRIS #2 and PRIS #3in the example of FIG. 2).

Thus, a repository of context related algorithms at the computer deviceresiding in the non-privileged network 2 may include a multitude ofcontext related algorithms from different sources. A user in any of theprivileged computer networks 1 a-1 c may create context relatedalgorithms using the clinical and patient data available in therespective privileged computer network 1 a-1 c and upload into therepository. These context related algorithms can then be obtained byusers in the other privileged computer networks 1 a-1 c (or the sameprivileged computer network 1 a-1 c). In other words, the computerdevice residing in the non-privileged network 2 can collect contextrelated algorithms generated by users in multiple privileged computernetworks (see also step 201 in FIG. 3).

In one example, a context related algorithm can be associated with adigital identifier identifying a source of the context related algorithm(e.g., a digital signature). In this manner, a user in a differentprivileged computer network can identify the source the context relatedalgorithm. As explained above, a user can select the context relatedalgorithms to be obtained (at least partially) based on the identity ofthe creator of the context related algorithm. In some examples, a usercan subscribe context related algorithm from one or more particularsources. The digital identifier identifying a source of the contextrelated algorithm can identify one or more of a particular person orgroup of persons being the creator of the context related algorithm oran organization or institution providing the context related algorithm(e.g., a particular laboratory, hospital or manufacturer of automatedlaboratory equipment).

The repository of context related algorithms at the computer deviceresiding in the non-privileged network 2 can be configured to allow fora continuous upload of context related algorithms. In this manner, therepository of context related algorithms can be continuouslysupplemented.

In addition or alternatively, the computer device residing in thenon-privileged network 2 can provide a rating system for users to ratethe quality of provided context related algorithms (e.g., a star ratingor a grade). In addition or alternatively, the computer device residingin the non-privileged network 2 can provide a platform to includecomments or additional information regarding provided context relatedalgorithms. The users of the repository can access the rating and/or thecomments to judge quality and usefulness of the respective contextrelated algorithms. This may be helpful to improve the value of thecontext related algorithms for the particular users.

In general, the computer device residing in the non-privileged network 2can provide an interface to access the context related algorithms in therepository and additional information (e.g., ratings or comments). Inone example, the interface may be web-based interface. In otherexamples, the computer devices of the privileged computer network caninterface with the repository on the computer device residing in thenon-privileged network 2 via an interface (e.g., an API). In someexamples, the interface with the repository can be integrated into ahospital or laboratory information system software, or softwarecontrolling one or more automated devices. In still other examples, thehospital or laboratory information system software or the softwarecontrolling one or more automated devices can interface with therepository automatically to obtain the context related algorithmsdescribed herein. In the following sections, the generation ofstatistical data (as described above) in network including multipleprivileged computer networks will be discussed.

As explained above, (anonymized) information regarding measurementresults can be provided to the computer device residing in thenon-privileged network 2 from other devices in privileged sources.

In this manner, the computer device residing in the non-privilegednetwork 2 can receive information regarding a plurality of results of aplurality of measurements of automated analyzers located in differentprivileged computer networks 1 a-1 c. In addition, the computer deviceresiding in the non-privileged network 2 can receive clinical data orpatient data associated with the plurality of measurements of automatedanalyzers.

The computer device residing in the non-privileged network 2 can collect(see also step 202 in FIG. 3) this data and generate (see also step 203in FIG. 3) statistical information associated with the plurality ofresults based on the information regarding the plurality of results(e.g., the statistical information discussed above).

This statistical information can then be provided (see also step 204 inFIG. 3) to the users to supplement measurement results, as alsodiscussed above.

For example, by collecting information from a plurality of sourcesunlikely measurement results can be identified. For example, adistribution of measurement values for a predetermined test or assay canbe compiled. This can be helpful to identify erroneous measurementresults.

In another example, information regarding a particular automatedanalyzer can be obtained by collecting information from a plurality ofsources. This can be helpful, e.g., to detect faulty devices or faultymaterial (e.g., disposable) used in the devices.

As described above, a user can obtain context related algorithms fromthe repository outside the privileged computer network or providecontext related algorithms to the repository outside the privilegedcomputer networks. In some examples, a user within the privilegedcomputer network can have one or more of the privileges comprisingobtaining a context related algorithm for the user, obtaining a contextrelated algorithm for all users within the privileged computer network,providing context related algorithms the user has created to thecomputer device residing outside of the privileged computer network forstorage and distribution to other privileged computer networks andproviding context related algorithms stored in the privileged computernetwork to the computer device residing outside of the privilegedcomputer network for storage and distribution to other privilegedcomputer networks.

In the preceding detailed description multiple examples of methods andsystems for supplementing measurement results of automated analyzers.However, the methods and systems for supplementing measurement resultsof automated analyzers of the present disclosure can also be configuredas the following.

A computer-implemented method for supplementing measurement results ofdiagnostic or laboratory automated analyzers is presented. The methodcan comprise obtaining at a computer device a result of a measurementperformed by a diagnostic or laboratory automated analyzer. The computerdevice and the automated analyzer can be located within a privilegedcomputer network. The method can also comprise obtaining a contextrelated algorithm associated with the result of the measurement definingone or more triggering conditions and context related information fromthe computer device residing outside of the privileged computer networkat the computer device and processing a result of a measurement by thediagnostic or laboratory automated analyzer by using the context relatedalgorithm to generate a context specific supplement to the result of themeasurement at the computer device. The processing of the result of themeasurement using the context related algorithm can be performedautomatically. The context related algorithm can be obtainedautomatically from the computer device residing outside of theprivileged computer network.

The method can further comprise selecting one or more sources whosecontext related algorithms can be obtained at the computer device and,before obtaining the context related algorithm, verifying the contextrelated algorithm stems from the selected one or more sources.

The selecting one or more sources can include accessing an interfaceprovided by the computer device residing outside of the privilegedcomputer network.

All context related algorithms from the selected one or more sources canbe obtained by the computer device automatically. The context relatedalgorithm can be associated with a digital identifier identifying asource of the context related algorithm. The digital identifier caninclude a digital signature.

The computer device residing outside of the privileged informationsystem can include a repository of context related algorithms definingone or more triggering conditions and context related information. Thecontext related algorithms in the repository can be created inprivileged computer networks.

The method can further comprise creating the context related algorithmat a computer device located in a second privileged computer networkdifferent from the privileged computer network. The computer deviceresiding outside of the privileged computer network can also resideoutside the second privileged computer network. The method can alsocomprise providing the context related algorithm to the computer deviceresiding outside of the privileged computer network for storage anddistribution to other privileged computer networks.

The context related algorithm can be created at a computer devicelocated in a second privileged computer network different from theprivileged computer network. The computer device residing outside of theprivileged computer network can also reside outside the secondprivileged computer network.

The method can further comprise presenting the context specificsupplement to the result of the measurement to a user. The presentingthe context specific supplement to the result of the measurement to auser can comprise presenting an indicator to the user that a contextspecific supplement is available for the result of the measurement on auser interface of the computer device and, in response to a userinteraction with the user interface of the computer device, presentingthe context specific supplement.

The method can further comprise providing information regarding theresult of the measurement to a computer device residing outside of theprivileged computer network.

The method can further comprise obtaining statistical informationassociated with the result of the measurement and adding the statisticalinformation associated with the result of the measurement to the contextspecific supplement. The statistical information associated with theresult of the measurement can include one or more of informationregarding: a likelihood of a combination of a measurement values in theresult of the measurement with other measurement values, a likelihood ofa combination of a measurement value in the result of the measurementwith a particular diagnosis, a likelihood of a combination of ameasurement value in the result with a particular disease progression orclinical finding, a likelihood of a combination of a measurement valuein the result of the measurement with particular biometric data,statistical information regarding other measurements carried out incombination with the measurement having yielded the result.

The method can further comprise obtaining statistical informationassociated with the context related algorithm and adding the statisticalinformation associated with the context related algorithm to the contextspecific supplement. The statistical information associated with thecontext related algorithm can include one or more of informationregarding a frequency with which the context related algorithm has beenobtained and statistical information related to a source of the contextrelated algorithm.

The method can further comprise receiving at the computer deviceresiding outside of the privileged computer network, informationregarding a plurality of results of a plurality of measurements ofdiagnostic or laboratory automated analyzers located in differentprivileged computer networks and generating statistical informationassociated with the plurality of results based on the informationregarding the plurality of results.

The method can further comprise evaluating the statistical informationassociated with the result of the measurement at the computer device andpresenting to a user one or more of an indication of likely diagnosesassociated with the result of the measurement, an indication of unlikelydiagnoses associated with the result of the measurement and anindication of frequently performed next diagnostic steps associated withthe result of the measurement.

The information regarding the result of the measurement provided to acomputer device residing outside of the privileged computer network caninclude one or more of one or more measurement values determined by thediagnostic or laboratory automated analyzer, one or more images obtainedby the diagnostic or laboratory automated analyzers, a description ofthe result of the measurement of the diagnostic or laboratory automatedanalyzer and additional information associated with the result of themeasurement. The information regarding the result of the measurement canbe anonymized.

The one or more triggering conditions can define one or more criteriafor the result of the measurement. The one or more criteria can includeone or more of a criterion evaluating a threshold for one or moremeasurement values, a criterion determining a relationship between twoor more measurement values and a criterion defining a pattern in aresult of a measurement.

The context related algorithm can be provided in a computer program codewhich can be executed by the computer device in the privileged computernetwork to generate a context specific supplement to the result of themeasurement.

The privileged computer network including at least the computer deviceand the diagnostic or laboratory automated analyzer can be protected bya firewall or other network protection. The privileged computer networkcan be a hospital information system or a laboratory information system.

The result of the measurement can be associated with patient-specificinformation. The patient specific information can be accessed fromwithin the privileged computer network but not from outside of theprivileged computer network.

The diagnostic or laboratory automated analyzer can be one of anin-vitro diagnostic analyzer, an ultrasound analyzer, an radiologydevice or a monitoring device of bodily functions or properties.

The computer device residing outside of the privileged computer networkcan be networked with a plurality of different privileged computernetworks. The method can further comprise obtaining results ofmeasurements from each of the plurality of different privileged computernetworks and providing context related algorithms to each of theplurality of different privileged computer networks.

The processing the result of the measurement by using the contextrelated algorithm to generate a context specific supplement includesprocessing patient data of a patient with which the result of themeasurement can be associated. The processing the result of themeasurement by using the context related algorithm to generate a contextspecific supplement can include processing one or more other results ofmeasurements associated with the result of the measurement.

The providing the information regarding the result of the measurementand/or obtaining a context related algorithm can be triggered by a userinteraction.

The context related algorithm can be stored in the privileged computernetwork for further processing operations of results of measurements.

A user within the privileged computer network can have one or more ofthe privileges comprising: obtaining a context related algorithm for theuser, obtaining a context related algorithm for all users within theprivileged computer network, providing context related algorithms theuser has created to the computer device residing outside of theprivileged computer network for storage and distribution to otherprivileged computer networks and providing context related algorithmsstored in the privileged computer network to the computer deviceresiding outside of the privileged computer network for storage anddistribution to other privileged computer networks.

The information regarding the result of the measurement can include aunique identifier. The information regarding the result of themeasurement can be free of patient identifiers.

A computer network can include a privileged computer network including afirst computer device and a diagnostic or laboratory automated analyzerand a second computer network outside of the privileged computernetwork. The computer network can be configured to carry out the abovemethod.

A computer-readable medium having instructions encoded thereon which,when executed by one or more computer devices can make the one or morecomputer devices perform the operations of the above method.

Further disclosed and proposed can be a computer program includingcomputer-executable instructions for performing the method according toone or more of the embodiments enclosed herein when the program can beexecuted on a computer or computer network. Specifically, the computerprogram may be stored on a computer-readable data carrier. Thus,specifically, one, more than one or even all of method steps asdisclosed herein may be performed by using a computer or a computernetwork by using a computer program.

Further disclosed and proposed is a computer program product havingprogram code, in order to perform the method according to one or more ofthe embodiments enclosed herein when the program can be executed on acomputer or computer network. Specifically, the program code may bestored on a computer-readable data carrier.

Further disclosed and proposed is a data carrier having a data structurestored thereon, which, after loading into a computer or computernetwork, such as into a working memory or main memory of the computer orcomputer network, may execute the method according to one or more of theembodiments disclosed herein.

Further disclosed and proposed is a computer program product withprogram code stored on a machine-readable carrier, in order to performthe method according to one or more of the embodiments disclosed herein,when the program is executed on a computer or computer network. As usedherein, a computer program product can refer to the program as atradable product. The product may generally exist in an arbitraryformat, such as in a paper format, or on a computer-readable datacarrier. Specifically, the computer program product may be distributedover a data network.

Further disclosed and proposed is a modulated data signal which containsinstructions readable by a computer system or computer network, forperforming the method according to one or more of the embodimentsdisclosed herein.

Referring to the computer-implemented aspects, one or more of the methodsteps or even all of the method steps of the method according to one ormore of the embodiments disclosed herein may be performed by using acomputer or computer network. Thus, generally, any of the method stepsincluding provision and/or manipulation of data may be performed byusing a computer or computer network. Generally, these method steps mayinclude any of the method steps, typically except for method stepsrequiring manual work, such as providing the samples and/or certainaspects of performing measurements.

Further disclosed and proposed is a computer or computer networkcomprising at least one processor. The processor can be adapted toperform the method according to one of the embodiments described in thisdescription.

Further disclosed and proposed is a computer loadable data structurethat can be adapted to perform the method according to one of theembodiments described in this description while the data structure isbeing executed on a computer.

Further disclosed and proposed is a storage medium. A data structure canbe stored on the storage medium. The data structure can be adapted toperform the method according to one of the embodiments described in thisdescription after having been loaded into a main and/or working storageof a computer or of a computer network.

It is noted that terms like “preferably,” “commonly,” and “typically”are not utilized herein to limit the scope of the claimed embodiments orto imply that certain features are critical, essential, or evenimportant to the structure or function of the claimed embodiments.Rather, these terms are merely intended to highlight alternative oradditional features that may or may not be utilized in a particularembodiment of the present disclosure.

Having described the present disclosure in detail and by reference tospecific embodiments thereof, it will be apparent that modifications andvariations are possible without departing from the scope of thedisclosure defined in the appended claims. More specifically, althoughsome aspects of the present disclosure are identified herein aspreferred or particularly advantageous, it is contemplated that thepresent disclosure is not necessarily limited to these preferred aspectsof the disclosure.

We claim:
 1. A system for providing context specific supplemental information to diagnostic laboratory measurement results, the system comprising: at least one privileged network, the privileged network comprising at least one monitoring device and at least one user computer terminal, wherein the monitoring device provides diagnostic laboratory measurement results from bodily functions to the at least one user computer terminal; and at least one non-privileged network, the non-privileged network comprising at least one computer device in communication with the at least one user computer terminal, wherein the at least one computer device provides a context related algorithm associated with the diagnostic laboratory measurement result, the context related algorithm defines one or more triggering conditions and context related information for the diagnostic laboratory measurement result provided by the monitoring device to the at least one user computer terminal, and wherein the at least one user computer terminal is configured to use the context related algorithm to generate context specific supplemental information regarding the diagnostic laboratory measurement results.
 2. The system according to claim 1, wherein the context related algorithm is a mobile app for installation on the at least one user computer terminal.
 3. The system according to claim 1, wherein the context related algorithm is an add-on to an already installed program on the at least one user computer terminal.
 4. The system according to claim 1, wherein the monitoring device is an automated laboratory analyzer.
 5. The system according to claim 1, wherein the at least one user computer terminal is a desktop computer.
 6. The system according to claim 1, wherein the at least one user computer terminal is a mobile device.
 7. The system according to claim 6, wherein the mobile device is a tablet device, laptop computer, a smartphone, or combinations thereof.
 8. The system according to claim 1, wherein the at least one privileged network is a hospital information system (HIS) or a laboratory information system (LIS).
 9. The system according to claim 1, wherein the at least one computer device within the non-privileged computer network is networked with a plurality of different privileged computer networks.
 10. The system according to claim 1, wherein the at least privileged network further comprises a firewall.
 11. The system according to claim 1, wherein the one or more triggering conditions comprises one or more criteria for the result of the measurement, wherein the one or more criteria comprise one or more of a criterion evaluating a threshold for one or more measurement values, a criterion determining a relationship between two or more measurement values and a criterion defining a pattern in a result of a measurement.
 12. The system according to claim 1, wherein the context specific supplemental information comprises textual information.
 13. The system according to claim 12, wherein the textual information comprises explanatory information regarding the interpreatation of the diagnostic laboratory measurement results, proposals regarding diagnostic steps to be undertaken, proposals regarding treatment steps, information regarding potential errors, or combinations thereof.
 14. The system according to claim 1, wherein the context specific supplemental information comprises multiple items of statistical information related to the diagnostic laboratory measurement results.
 15. The system according to claim 1, wherein the context specific supplemental information comprises audible, visual, and/or audiovisual warnings.
 16. The system according to claim 1, wherein the at least one computer device in the at least one non-privileged network comprises a repository of context related algorithms.
 17. The system according to claim 16, wherein the repository of context related algorithms comprises a multitude of context related algorithms from different sources.
 18. The system according to claim 16, wherein the repository of context related algorithms is continuously supplemented.
 19. The system according to claim 16, wherein at least one computer device in the at least one non-privileged network comprises a rating system for the repository of context related algorithms.
 20. The system according to claim 1, wherein the diagnostic laboratory measurement results comprise a unique identifier and wherein the diagnostic laboratory measurement results is free of patient identifiers. 